A Novel Automated Guided Vehicle (AGV) Remote Path Planning Based on RLACA Algorithm in 5G Environment

Authors

  • Wangwang Yu School of Electrical Engineering, Shanghai DianJi University. Shanghai 201306, China https://orcid.org/0000-0003-4498-4120
  • Jun Liu School of Electrical Engineering, Shanghai DianJi University. Shanghai 201306, China
  • Jie Zhou School of Electrical Engineering, Shanghai DianJi University. Shanghai 201306, China

DOI:

https://doi.org/10.13052/jwe1540-9589.20813

Keywords:

5G, path planning, ant colony algorithm, reinforcement learning, path correction

Abstract

Remote control and monitoring will become the future trend. High-quality automated guided vehicle (AGV) path planning through web pages or clients can reduce network data transmission capacity and server resource occupation. Many Remote path planning algorithms in AGV navigation still have blind search, path redundancy, and long calculation time. This paper proposed an RLACA algorithm based on 5G network environment through remote control of AGV. The distribution of pheromone in each iteration of the ant colony algorithm had an impact on the follow-up. RLACA algorithm changed the transfer rules and pheromone distribution of the ant colony algorithm to improve the efficiency of path search and then modify the path to reduce path redundancy. Considering that there may be unknown obstacles in the virtual environment, the path obtained by the improved ant colony algorithm is used as the training data of reinforcement learning to obtain the Q-table. During the movement, the action of each step is selected by the Q-table until the target point is reached. Through experimental simulation, it can be concluded that the enhanced ant colony algorithm can quickly obtain a reasonable and adequate path in a complex environment and effectively avoid unknown obstacles in the environment.

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Author Biographies

Wangwang Yu, School of Electrical Engineering, Shanghai DianJi University. Shanghai 201306, China

Wangwang Yu is a Master’s Degree candidate at the School of Electrical Engineering, Shanghai Dianji University. He received his BS in Anhui Agricultural University, Hefei, in 2019. His research interest is robot control system.

Jun Liu, School of Electrical Engineering, Shanghai DianJi University. Shanghai 201306, China

Jun Liu is presently a Professor at the School of Electrical Engineering, Shanghai Dianji University, China. He received his Ph.D. in East China University of Science and Technology, Shanghai, in 2010. His current projects are in the areas of intelligent control and microcomputer control technology.

Jie Zhou, School of Electrical Engineering, Shanghai DianJi University. Shanghai 201306, China

Jie Zhou He is currently pursuing the master degree in electrical engineering with China Shanghai Dian Ji University. His research interests include PMSM sensorless control and power electronics.

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Published

2021-11-22

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